# A tibble: 6 x 24
Group ID Genotype LV_Vol_s LV_Vol_d EF FS Dia_s Dia_d CO
<chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 June 2408 Wild_Ty… 24.9 64.4 61.3 32.3 2.61 3.86 22.2
2 June 2409 Wild_Ty… 27.5 77.9 64.7 35 2.72 4.18 26
3 June 2406 Mutant 59.3 105. 43.7 21.6 3.73 4.76 25
4 June 2407 Wild_Ty… 23.9 67.6 64.7 34.8 2.57 3.94 23.2
5 June 2420 Wild_Ty… 25.4 63.4 60 31.3 2.64 3.83 19
6 June 2418 Mutant 25.4 68.2 62.8 33.4 2.63 3.96 20.8
# … with 14 more variables: HR <dbl>, LVAW_d <dbl>, LVAW_s <dbl>,
# LVPW_d <dbl>, LVPW_s <dbl>, LVOT_mean_grad <dbl>, LVOT_mean_vel <dbl>,
# LVOT_Peak_grad <dbl>, LVOT_Peak_vel <dbl>, Aor_sys <dbl>,
# Aor_dia <dbl>, Aor_FS <dbl>, brach <dbl>, PWV <dbl>
Group ID Genotype LV_Vol_s
Length:42 Min. :2247 Length:42 Min. :16.00
Class :character 1st Qu.:2408 Class :character 1st Qu.:25.32
Mode :character Median :2420 Mode :character Median :30.35
Mean :2420 Mean :33.96
3rd Qu.:2488 3rd Qu.:42.38
Max. :2493 Max. :61.00
NA's :2
LV_Vol_d EF FS Dia_s
Min. : 45.20 Min. :24.60 Min. :11.20 Min. :2.189
1st Qu.: 67.35 1st Qu.:51.55 1st Qu.:26.32 1st Qu.:2.629
Median : 77.45 Median :60.45 Median :32.00 Median :2.830
Mean : 77.70 Mean :57.32 Mean :30.11 Mean :2.921
3rd Qu.: 83.90 3rd Qu.:63.08 3rd Qu.:33.77 3rd Qu.:3.244
Max. :105.40 Max. :74.00 Max. :42.00 Max. :3.775
NA's :2 NA's :2 NA's :2 NA's :2
Dia_d CO HR LVAW_d
Min. :3.334 Min. : 9.20 Min. :416.0 Min. :0.8280
1st Qu.:3.935 1st Qu.:20.00 1st Qu.:478.5 1st Qu.:0.8750
Median :4.175 Median :22.10 Median :518.0 Median :0.9310
Mean :4.166 Mean :22.62 Mean :516.9 Mean :0.9631
3rd Qu.:4.317 3rd Qu.:25.00 3rd Qu.:554.5 3rd Qu.:1.0243
Max. :4.759 Max. :33.00 Max. :616.0 Max. :1.2800
NA's :2 NA's :2 NA's :2 NA's :2
LVAW_s LVPW_d LVPW_s LVOT_mean_grad
Min. :1.350 Min. :0.8000 Min. :1.270 Min. : 1.200
1st Qu.:1.380 1st Qu.:0.8552 1st Qu.:1.312 1st Qu.: 2.175
Median :1.406 Median :0.9260 Median :1.375 Median : 2.650
Mean :1.435 Mean :0.9519 Mean :1.376 Mean : 4.048
3rd Qu.:1.448 3rd Qu.:1.0083 3rd Qu.:1.400 3rd Qu.: 3.550
Max. :1.706 Max. :1.2830 Max. :1.622 Max. :17.700
NA's :2 NA's :2 NA's :2 NA's :2
LVOT_mean_vel LVOT_Peak_grad LVOT_Peak_vel Aor_sys
Min. : 555.0 Min. : 2.800 Min. : 839 Min. :1.440
1st Qu.: 743.5 1st Qu.: 5.475 1st Qu.:1169 1st Qu.:1.678
Median : 818.0 Median : 7.050 Median :1326 Median :1.885
Mean : 941.2 Mean : 9.863 Mean :1477 Mean :1.942
3rd Qu.: 943.2 3rd Qu.: 8.350 3rd Qu.:1446 3rd Qu.:2.083
Max. :2103.0 Max. :36.000 Max. :2995 Max. :3.200
NA's :2 NA's :2 NA's :2 NA's :5
Aor_dia Aor_FS brach PWV
Min. :1.270 Min. :0.0500 Min. :0.6570 Min. :2.800
1st Qu.:1.479 1st Qu.:0.0745 1st Qu.:0.7847 1st Qu.:3.590
Median :1.620 Median :0.1165 Median :0.8945 Median :3.880
Mean :1.713 Mean :0.1092 Mean :0.9248 Mean :4.129
3rd Qu.:1.889 3rd Qu.:0.1398 3rd Qu.:0.9832 3rd Qu.:4.320
Max. :3.031 Max. :0.1790 Max. :1.6310 Max. :8.400
NA's :6 NA's :6 NA's :6 NA's :25
[1] "Group" "ID" "Genotype" "LV_Vol_s"
[5] "LV_Vol_d" "EF" "FS" "Dia_s"
[9] "Dia_d" "CO" "HR" "LVAW_d"
[13] "LVAW_s" "LVPW_d" "LVPW_s" "LVOT_mean_grad"
[17] "LVOT_mean_vel" "LVOT_Peak_grad" "LVOT_Peak_vel" "Aor_sys"
[21] "Aor_dia" "Aor_FS" "brach" "PWV"
$LV_Vol_s
Two Sample t-test
data: x by EchoC_data$Genotype
t = 5.9496, df = 38, p-value = 6.676e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
10.96673 22.27888
sample estimates:
mean in group Mutant mean in group Wild_Type
42.68947 26.06667
$LV_Vol_d
Two Sample t-test
data: x by EchoC_data$Genotype
t = 3.8686, df = 38, p-value = 0.0004162
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
6.819928 21.792603
sample estimates:
mean in group Mutant mean in group Wild_Type
85.21579 70.90952
$EF
Two Sample t-test
data: x by EchoC_data$Genotype
t = -6.0947, df = 38, p-value = 4.221e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-17.665947 -8.856358
sample estimates:
mean in group Mutant mean in group Wild_Type
50.35789 63.61905
$FS
Two Sample t-test
data: x by EchoC_data$Genotype
t = -6.3226, df = 38, p-value = 2.057e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.209337 -5.772117
sample estimates:
mean in group Mutant mean in group Wild_Type
25.64737 34.13810
$Dia_s
Two Sample t-test
data: x by EchoC_data$Genotype
t = 6.1376, df = 38, p-value = 3.686e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.3919792 0.7778153
sample estimates:
mean in group Mutant mean in group Wild_Type
3.228421 2.643524
$Dia_d
Two Sample t-test
data: x by EchoC_data$Genotype
t = 3.8211, df = 38, p-value = 0.0004782
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.1513222 0.4923119
sample estimates:
mean in group Mutant mean in group Wild_Type
4.334579 4.012762
$CO
Two Sample t-test
data: x by EchoC_data$Genotype
t = -0.84613, df = 38, p-value = 0.4028
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.902695 1.601943
sample estimates:
mean in group Mutant mean in group Wild_Type
22.02105 23.17143
$HR
Two Sample t-test
data: x by EchoC_data$Genotype
t = 0.18047, df = 38, p-value = 0.8577
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-29.06393 34.75315
sample estimates:
mean in group Mutant mean in group Wild_Type
518.3684 515.5238
$LVAW_d
Two Sample t-test
data: x by EchoC_data$Genotype
t = 5.6284, df = 38, p-value = 1.841e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.09635995 0.20461248
sample estimates:
mean in group Mutant mean in group Wild_Type
1.042105 0.891619
$LVAW_s
Two Sample t-test
data: x by EchoC_data$Genotype
t = 3.1531, df = 38, p-value = 0.00315
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.02709746 0.12429603
sample estimates:
mean in group Mutant mean in group Wild_Type
1.474316 1.398619
$LVPW_d
Two Sample t-test
data: x by EchoC_data$Genotype
t = 4.6126, df = 38, p-value = 4.418e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.07852254 0.20135716
sample estimates:
mean in group Mutant mean in group Wild_Type
1.0253684 0.8854286
$LVPW_s
Two Sample t-test
data: x by EchoC_data$Genotype
t = 2.6399, df = 38, p-value = 0.01196
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.01299953 0.09850423
sample estimates:
mean in group Mutant mean in group Wild_Type
1.404895 1.349143
$LVOT_mean_grad
Two Sample t-test
data: x by EchoC_data$Genotype
t = 3.4472, df = 38, p-value = 0.001398
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1.477108 5.680286
sample estimates:
mean in group Mutant mean in group Wild_Type
5.926316 2.347619
$LVOT_mean_vel
Two Sample t-test
data: x by EchoC_data$Genotype
t = 3.94, df = 38, p-value = 0.0003372
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
186.0828 579.3759
sample estimates:
mean in group Mutant mean in group Wild_Type
1142.1579 759.4286
$LVOT_Peak_grad
Two Sample t-test
data: x by EchoC_data$Genotype
t = 3.8084, df = 38, p-value = 0.0004963
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
4.161416 13.605502
sample estimates:
mean in group Mutant mean in group Wild_Type
14.526316 5.642857
$LVOT_Peak_vel
Two Sample t-test
data: x by EchoC_data$Genotype
t = 4.4382, df = 38, p-value = 7.541e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
339.5570 909.1046
sample estimates:
mean in group Mutant mean in group Wild_Type
1804.474 1180.143
$Aor_sys
Two Sample t-test
data: x by EchoC_data$Genotype
t = 5.9821, df = 35, p-value = 8.152e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.3407812 0.6908971
sample estimates:
mean in group Mutant mean in group Wild_Type
2.192895 1.677056
$Aor_dia
Two Sample t-test
data: x by EchoC_data$Genotype
t = 6.2424, df = 34, p-value = 4.18e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.3419442 0.6720558
sample estimates:
mean in group Mutant mean in group Wild_Type
1.966056 1.459056
$Aor_FS
Two Sample t-test
data: x by EchoC_data$Genotype
t = -4.1162, df = 34, p-value = 0.0002318
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.06323398 -0.02143268
sample estimates:
mean in group Mutant mean in group Wild_Type
0.0880000 0.1303333
$brach
Two Sample t-test
data: x by EchoC_data$Genotype
t = 5.4092, df = 34, p-value = 5.052e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.1851729 0.4080494
sample estimates:
mean in group Mutant mean in group Wild_Type
1.073111 0.776500
$PWV
Two Sample t-test
data: x by EchoC_data$Genotype
t = 2.5485, df = 15, p-value = 0.02226
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.220428 2.473461
sample estimates:
mean in group Mutant mean in group Wild_Type
4.842500 3.495556
Dashboard made by Jacob Noeker:
---
title: "Data Viz Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: columns
source_code: embed
---
```{r setup, include=FALSE}
library(tidyverse)
library(readxl)
library(ggplot2)
library(plotly)
knitr::opts_chunk$set(message = FALSE)
```
Original Data {vertical_layout=scroll data-icon="fa-archive"}
=====================================
Row {data-height=10000}
------------------------------------------------------------------------------
### A look at the data
```{r raw data}
EchoC_data <- read_excel("Pfeifer Echo Edit for R.xlsx", sheet = "Echo_for_R")
head(EchoC_data)
summary(EchoC_data)
names(EchoC_data)
```
Column {data-height=10000}
------------------------------------------------------------------------------
### Running some statistics
```{r stats}
stats_1<-lapply(EchoC_data[,4:24], function(x) t.test(x ~ EchoC_data$Genotype, var.equal = TRUE))
stats_1
```
Visualizations {data-orientation=columns data-icon="fa-chart-bar"}
=====================================
Row {.tabset data-height=400}
------------------------------------------------------------------------------
### Early violin plots
```{r Early Data}
LV_Sys_Vol_Sept <- ggplot(EchoC_data, aes(x = Genotype, y = LV_Vol_s)) + geom_violin() + theme_bw() +
ylab("Left Ventricular Systolic Volume (uL)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant"), label = c("Wild Type", "Mutant")) +
ggtitle("September 2018 Systolic Volume Variation") +
theme(plot.title = element_text(hjust = 0.5)) +
stat_summary(fun.y = mean, geom="point", shape = 18, size = 3) +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue")
LV_Sys_Vol_Sept
LV_Diameter_Sept <- ggplot(EchoC_data, aes(x = Genotype, y = Dia_s)) + geom_violin() + theme_bw() +
ylab("Left Ventricular Diameter (mm)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant"), label = c("Wild Type", "Mutant")) +
ggtitle("September 2018 Diameter Variation During Systole") +
theme(plot.title = element_text(hjust = 0.5)) +
stat_summary(fun.y = mean, geom="point", shape = 18, size = 3) +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue")
LV_Diameter_Sept
```
Row {.tabset data-height=400}
------------------------------------------------------------------------------
### Poster violin plots
```{r poster graphs}
jn_theme <- function(){
theme_bw() +
theme(axis.text = element_text(size = 14, color = "Black"),
axis.title = element_text(size = 16),
panel.grid.minor = element_blank(),
strip.text = element_text(size=14),
strip.background = element_blank(),
plot.title = element_text(size = 20, hjust = 0.5),
panel.grid.major.x = element_blank())
}
LV_Sys_Vol_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = LV_Vol_s)) +
geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) +
scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Left Ventricular Systolic Volume (uL)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
stat_summary(fun.y = mean, geom = "point", shape = 5, size = 3, color = "#9A2617") +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue") +
jn_theme() +
theme(axis.title.x = element_blank())
LV_Sys_Vol_15
#Dont need this for poster
LV_Diam_systole_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = Dia_s)) +
geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) +
scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Left Ventricular Diameter (mm)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
stat_summary(fun.y = mean, geom = "point", shape = 5, size = 3, color = "#9A2617") +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue") +
jn_theme() +
theme(axis.title.x = element_blank())
LV_Diam_systole_15
LVAW_dia_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = LVAW_d)) +
geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) +
scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Left Ventricular Anterior Wall Diameter (mm)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
stat_summary(fun.y = mean, geom = "point", shape = 5, size = 3, color = "#9A2617") +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue") +
jn_theme() +
theme(axis.title.x = element_blank())
LVAW_dia_15
Ejection_fraction_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = EF)) +
geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) +
scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Ejection Fraction (%)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
stat_summary(fun.y = mean, geom = "point", shape = 5, size = 3, color = "#9A2617") +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue") +
jn_theme() +
theme(axis.title.x = element_blank())
Ejection_fraction_15
LVOT_Mean_grad_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = LVOT_mean_grad)) +
geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) +
scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Mean Gradient (mmHg)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
stat_summary(fun.y = mean, geom = "point", shape = 5, size = 3, color = "#9A2617") +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue") +
jn_theme() +
theme(axis.title.x = element_blank())
LVOT_Mean_grad_15
Aorta_sys_15<- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = Aor_sys)) +
geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) +
scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Aorta Diameter (mm)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
stat_summary(fun.y = mean, geom = "point", shape = 5, size = 3, color = "#9A2617") +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue") +
jn_theme() +
theme(axis.title.x = element_blank())
Aorta_sys_15
```
Row {.tabset data-height=400}
------------------------------------------------------------------------------
### Tidied paired violin plots
```{r plots}
jn_theme <- function(){
theme_bw() +
theme(axis.text = element_text(size = 14, color = "Black"),
axis.title = element_text(size = 16),
panel.grid.minor = element_blank(),
strip.text = element_text(size=14),
strip.background = element_blank(),
plot.title = element_text(size = 20, hjust = 0.5),
panel.grid.major.x = element_blank())
}
LV_EF <- ggplot(EchoC_data, aes(x = Genotype, y = EF, fill = Group)) +
geom_violin(scale = "area", adjust = 1) +
scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
theme_bw() +
geom_point(position=position_dodge(width = 0.9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Ejection Fraction %") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
ggtitle("Ejection Fraction Variation") +
theme(plot.title = element_text(hjust = 0.5)) +
stat_summary(fun.y = mean, geom="point", shape = 18, size = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) +
stat_summary(fun.y=median, geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
jn_theme()
LV_EF
LVOT_Mean_grad <- ggplot(EchoC_data, aes(x = Genotype, y = LVOT_mean_grad, fill = Group)) +
geom_violin(scale = "area", adjust = 1) +
scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
theme_bw() +
geom_point(position=position_dodge(width = 0.9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) +
ylab("Left Ventricular Outflow Tract Mean Gradient") + #UNITS???
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
ggtitle("Left Ventricular Outflow Tract Mean Gradient Variation") +
theme(plot.title = element_text(hjust = 0.5)) +
stat_summary(fun.y = mean, geom="point", shape = 18, size = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) +
stat_summary(fun.y=median, geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
jn_theme()
LVOT_Mean_grad
Aorta_sys <- ggplot(EchoC_data, aes(x = Genotype, y = Aor_sys, fill = Group)) +
geom_violin(scale = "area", adjust = 1) +
scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
theme_bw() +
geom_point(position=position_dodge(width = 0.9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) +
ylab("Aorta Diameter (mm)") + #UNITS???
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
ggtitle("Variations in Aortic Diameter in Systole") +
theme(plot.title = element_text(hjust = 0.5)) +
stat_summary(fun.y = mean, geom="point", shape = 18, size = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) +
stat_summary(fun.y=median, geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
jn_theme()
Aorta_sys
```
Interactive Plots {data-orientation=columns data-icon="fa-eye"}
=====================================
Column {data-width=500}
------------------------------------------------------------------------------
### Using plotly
```{r interactive}
jn_theme <- function(){
theme_bw() +
theme(axis.text = element_text(size = 14, color = "Black"),
axis.title = element_text(size = 16),
panel.grid.minor = element_blank(),
strip.text = element_text(size=14),
strip.background = element_blank(),
plot.title = element_text(size = 20, hjust = 0.5),
panel.grid.major.x = element_blank())
}
LV_Sys_Vol <- ggplot(EchoC_data, aes(x = Genotype, y = LV_Vol_s, fill = Group, label = ID)) +
geom_violin() +
xlim(0, 20) +
scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
geom_point(position=position_dodge(width = .9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
ylab("Left Ventricular Systolic Volume (μL)") +
scale_x_discrete(limits = c("Wild_Type", "Mutant")) +
ggtitle("Systolic Volume Variation") +
stat_summary(fun.y = mean, geom = "point", shape = 18, size = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) +
stat_summary(fun.y = median, geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
jn_theme()
LV_Sys_Vol
#Messing around:
library(plotly)
ggplotly(LV_Sys_Vol, tooltip = c("ID", "LV_Vol_s"))%>%layout(violinmode = 'group', violingap = 1, violingroupgap = 1)
```
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Dashboard made by Jacob Noeker:
* Made for Data Visualization in R
* Thank you to Abhijit Dasgupta